Simulation of the spatial distribution of urban populations based on first-aid call data
We examined the feasibility of estimating the spatial distribution of urban populations based on first-aid calls based on one high-density place, the Shanghai urban area and one low-density place, the Nanhai District of Foshan City in Guangdong Province. We aggregated the population and the total number of first-aid calls on digital maps divided by grids based on a Geographic Information System (GIS). Geographically weighted regression was applied to test the correlation between the population distribution simulated by first-aid call data and the actual residency. The impact of different population densities, different grid cell sizes and different types of first-aid calls on simulation correlation were tested. We found that the use of first-aid call data could explain 60-95% of the actual population distribution in Shanghai using a grid with 1000*1000 m cell size, while the Nanhai experience was that first-aid calls could only explain 4-76% of the actual population distribution using a grid with 2000*2000 m cell size. Thus, the higher the population density, the better the simulation effect. For a high-population density area, the overall accuracy of simulation can reach as high as 0.878 at the 1-km2 resolution. However, there are different kinds of first-aid calls and for the best estimation of the population distribution in densely populated areas, we suggest using first-aid calls from people requiring acute medical care rather than all first-aid call data.
Athavan, K., et al. (2012). Automatic Ambulance Rescue System. Second International Conference on Advanced Computing & Communication Technologies. DOI: https://doi.org/10.1109/ACCT.2012.34
Bhaduri B, Bright, E, Coleman P, Urban M, 2007. LandScan USA: a high-resolution geospatial and temporal modeling approach for population distribution and dynamics. GeoJournal 69(1-2), 103-17. DOI: https://doi.org/10.1007/s10708-007-9105-9
Cai, Q., et al. (2006). "Estimating Small‐Area Populations by Age and Sex Using Spatial Interpolation and Statistical Inference Methods." Transactions in GIS 10(4): 577-598. DOI: https://doi.org/10.1111/j.1467-9671.2006.01013.x
Dobson, J. E., et al. (2000). "LandScan: a global population database for estimating populations at risk." Photogrammetric engineering and remote sensing 66(7): 849-857.
Hay, S. I., et al. (2010). "The accuracy of human population maps for public health application." 10(10): 1073-1086.
Jia, P. (2012). Modeling High-Resolution Gridded Population Surface in Alachua County, Florida.
Linard, C., et al. (2012). "Population distribution, settlement patterns and accessibility across Africa in 2010." 7(2): e31743.
Martin, D. (1989). "Mapping population data from zone centroid locations." Transactions of the Institute of British Geographers 14(1): 90-97. DOI: https://doi.org/10.2307/622344
Mintsis, G., et al. (2004). "Applications of GPS technology in the land transportation system." 152(2): 399-409.
Reed, F., et al. (2018). "Gridded population maps informed by different built settlement products." 3(3): 33.
Tatem, A. J., et al. (2011). "The effects of spatial population dataset choice on estimates of population at risk of disease." 9(1): 4.
The Economist, 2011. "Censuses: Costing the count." available at https://www.economist.com/international/2011/06/02/costing-the-count accessed 20200130.
The Ledger, 2010. "US Census Takers Attacked on the Job." aNational Ledger. available at https://nationalledger.com/2010/05/us-census-takers-attacked-on-the-job/ accessed 20200130.
Tobler, W. R. (1979). "Smooth pycnophylactic interpolation for geographical regions." Journal of the American Statistical Association 74(367): 519-530. DOI: https://doi.org/10.1080/01621459.1979.10481647
Wang, X.-m., et al. (2004). "Advance and case analysis in population spatial distribution based on remote sensing and GIS." Remote Sensing Technology and Application 5: 006.
Wardrop, N. A., et al. (2018). "Spatially disaggregated population estimates in the absence of national population and housing census data." 115(14): 3529-3537.
Wei, Q. I., et al. (2015). "Modeling the spatial distribution of urban population during the daytime and at night based on land use:A case study in Beijing, China." 25(6): 756-768.
Yang, H. J. J. o. A. A. S. (2011). "Simulation of Urban Population Distribution during Daytime Based on the High Resolution of RS and GIS."
Yang, X., et al. (2002). "Regionalization of population distribution based on spatial analysis." J. Geogr. Sci 57: 76-81.
Yao, H. W., et al. (2011). "Application of GIS on Emergency Rescue." 11(11): 185-188.
Zhou, Y., et al. (2018). "Development of a hexagonal, mesh-based distribution method for community health centres." 13(2). DOI: https://doi.org/10.4081/gh.2018.648
- Abstract views: 416
- PDF: 121
Copyright (c) 2020 The Authors
This work is licensed under a Creative Commons Attribution-NonCommercial 4.0 International License.